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  Facial features based human face recognition methodUSPTO Application #: 20070071288Title: Facial features based human face recognition method Abstract: A method of facial features based human face recognition is disclosed. A human face and facial features thereof with respect to an input image corresponding to a person are first detected person by person by image processing technology. Then, each of such facial features for a plurality of persons are categorized into several categories and expressed to form a human facial features database for the plurality of persons. A to-be-searched or recognized human face image of a person is inputted. Then, the image is acquired with positions of the person's face and facial features by image processing technology and each of the facial features is categorized into several categories each with a specific expression. Then, according to the categories which the facial features of the person belongs to, the person may be recognized. As such, the purposes of human face search and recognition are achieved. (end of abstract)
Agent: Rabin & Berdo, P. C. - Washington, DC, US Inventors: Quen-Zong Wu, Heng-Sung Liu, Chia-Jung Pai USPTO Applicaton #: 20070071288 - Class: 382118000 (USPTO) Related Patent Categories: Image Analysis, Applications, Personnel Identification (e.g., Biometrics), Using A Facial Characteristic The Patent Description & Claims data below is from USPTO Patent Application 20070071288. Brief Patent Description - Full Patent Description - Patent Application Claims
BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] The present invention relates to a method of facial features based human face recognition through which positions of a human face and facial features thereof may be automatically detected and the facial features may be categorized by using image processing technology, which may be widely used in face search and recognition. [0003] 2. Description of the Prior Art [0004] For the bio features authentication systems or human face recognition systems, image processing technology is generally applied to achieve the human face recognition function. In those systems, a human face image database should be established previously, which is waste of time, and an objective human face is compared with human faces stored in the database. However, since the comparison process is waste of time and resource, and no any visual expression with respect to an identified person are made previously. It is difficult to determine whether the objective human face is the same to one of the image information batches stored in the databases for a human being. In addition, there is no method existing to describe human facial features. In view of this, the conventional systems are not user-friendly to users and needed to be improved. [0005] From the above discussion, it can be readily known that some drawbacks are inherent in such conventional bio features authentication systems or human face recognition systems and need to be addressed and improved. [0006] In view of these problems encountered in the prior art, the Inventors have paid many efforts in the related research and finally developed successfully a method of facial features based human face recognition which may be implemented in bio features authentication systems or human face recognition systems. In this method, human facial features may be detected by using image processing technology and categorized. Further, the method provides a reasonable and good human facial description manner. SUMMARY OF THE INVENTION [0007] It is, therefore, an object of the present invention to provide a method of facial features based human face recognition which may improve the prior art, bio features authentication systems and human face recognition systems, and provide a reasonable and practicable solution to describe human faces. [0008] Since the conventional human face recognition system or bio features authentication system is provided for recognition or authentication of human beings or organisms and thus has a different facial features description manner as compared to that generally used. A user may think the previous system is not intuitive and not friendly, and the system may not be readily used in real environment. To overcome the disadvantages of the prior art system, a method of facial features based face recognition is set forth in the present invention. [0009] The inventive system is mainly composed of a human face detection unit and a human facial features description unit. An human face image is inputted into the human face detection unit and processed by a human face detection algorithm, through which a portion of the person where the human face is located is acquired and positions of his/her human face features, such as eyes, nostrils, ears and mouth, are detected. [0010] The human facial features description unit has categories defined for each of the facial features. For example, eyes may have the categories of small eyes, big eyes and single eye and mouth may have the categories of small mouth, big mouth and mouth of thick lips. [0011] With the inventive features expression method, the current bio features authentication system and human face recognition system may define sufficient and reasonable categories for each of the human facial features. With these categories, not only authentication function but also a more proper description manner of human facial features may be achieved in the system. Further, a possible object may be effectively located when a habitually practiced oral description manner of human beings is inputted. Therefore, the inventive method possesses an improved usage and communication interface. [0012] These features and advantages of the present invention will be fully understood and appreciated from the following detailed description of the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS [0013] The drawings disclose an illustrative embodiment of the present invention which serves to exemplify the various advantages and objects hereof, and are as follows: [0014] FIG. 1 is an architecture diagram of the system, on which a method of facial features based human face recognition according to an embodiment of the present invention is performed; [0015] FIG. 2A.about.FIG. 2H is human facial features diagram illustrating the method of facial features based human face recognition according to the embodiment of the present invention; [0016] FIG. 3A.about.FIG. 3F is a schematic diagram of categories of mouth according to the present invention; and [0017] FIG. 4 is a schematic diagram of a combination of various classifiers according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT [0018] According to the present invention, a method of facial features based human face recognition, which is used to recognize an input human face image corresponding to a person, is set forth and characterized in that positions of a human face and all the facial features of the human face are detected by a human face detection unit and each of the facial features is categorized into one of a plurality of categories with respect to the facial feature by a human facial features description unit. Each of the facial features has its pre-defined expression, so that the input human face image is recognized in terms of each of the facial features thereof, and the determined category of each facial feature is compared to those of all persons stored in a database. The database obtained in the same way as that for the person has the determined category for each of the facial features for a plurality of persons, and the person can be identified by matching with people in the database. [0019] Referring to FIG. 1 and FIG. 2A.about.FIG. 2H, an architecture diagram of the system, on which the method of facial features based human face recognition is performed, and an exemplary case of the method according to a preferred embodiment of the present invention are shown therein, respectively. At first, a human face image is inputted to a human face detection unit 11. As an example, the inputted image, two consecutively taken photographs, is shown in FIG. 2A and FIG. 2B, respectively. In the human face detection unit 11, a human face positioning sub-unit 13 and a human facial features acquiring sub-unit 14 are comprised. The human face positioning sub-unit 13 is used to determine a contour of an object to be detected by using moving object detection and edge image detection methods, shown in FIG. 2C and FIG. 2D. Then, ellipse positioning and skin tone detection algorithms are used to detect the position of the human face, shown in FIG. 2E. The human face features acquiring unit 14 is used to detect facial features to be categorized, such as eyes, nostrils, ears and mouth. Each human facial feature is categorized into several categories previously defined. Hereinbelow, only eyes and mouth are explained in terms of position detection as examples by using an eyes mask depicted in the following. As such, a possible position of the eyes or mouth may be located. [0020] The first mask has a dimension of P.times.2 Q and is used to locate a center point having a darker rectangular block above and a brighter rectangular block below. The second mask has a dimension of P.times.Q and is used to locate a center point having a brighter rectangular block central to the center point and two rectangular blocks at both sides of the center point. If the two mask operation results are both greater than a threshold .rho. at the same bit point, then the bit point is considered as a center position of the eyes. For this reason, the two masks are named as eyes' center masks. When the eyes' center position is located, the position of the eyes has to be further confirmed. Since many candidate points are presented, the exact positions of the eyes and their centers are needed to be located further. At this time, local minimums on horizontal and vertical lines are taken from the human face area, and the minimums on the horizontal and vertical lines are AND-ed so as to obtain several candidate points. By using connected component labeling method, the located positions of the eyes are divided into several blocks of eyes' center. Then, eyes match is conducted over two sides of each of the block. The eyes match is done when the following three conditions are met. 1. The position of the center of the matched eyes has to fall on the block of eyes' center. 2. The matched eyes have to have similar averages values of gray level. 3. Tilt angle of the matched eyes has to be within an acceptable range. Since many eyes may be still matched according to the above three conditions, the final matched eyes have the minimum distance but greater than a threshold .rho.. As such, the position of the matched eyes is located by means of the block of eyes' center. Finally, a block with matched eyes which is closest to the center of face is determined as the proper block of eyes' center. In FIG. 2F, a black block is the possible block of eye's center and a grey point is a local minimum. Continue reading... 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